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Dockerfile

@@ -0,0 +1,10 @@
+FROM python:3.10-slim
+
+WORKDIR /app
+
+COPY requirements.txt .
+RUN pip install --no-cache-dir -r requirements.txt
+
+COPY . .
+
+CMD ["uvicorn", "app:app", "--host", "0.0.0.0", "--port", "7860"]

README.md

@@ -1,11 +1,32 @@
 ---
-title: Clickbait Detector Api
-emoji: 📊
-colorFrom: indigo
-colorTo: red
+title: Clickbait Detector API
+emoji: 🎯
+colorFrom: purple
+colorTo: pink
 sdk: docker
 pinned: false
-license: apache-2.0
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# Clickbait Detector API
+
+AI-powered API to detect and neutralize clickbait headlines using DistilBERT and T5.
+
+## Endpoints
+
+- `GET /` - API info
+- `POST /detect` - Detect if headline is clickbait
+- `POST /rewrite` - Rewrite clickbait to neutral
+- `POST /analyze` - Detect + rewrite in one call
+
+## Usage
+```python
+import requests
+
+response = requests.post(
+    "https://YOUR-SPACE.hf.space/detect",
+    json={"headline": "You Won't Believe What Happened!"}
+)
+print(response.json())
+```
+
+Powered by DistilBERT and T5.

app.py

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+from fastapi import FastAPI
+from fastapi.middleware.cors import CORSMiddleware
+from pydantic import BaseModel
+import torch
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+from transformers import T5Tokenizer, T5ForConditionalGeneration
+import os
+
+app = FastAPI(title="Clickbait Detector API")
+
+# Enable CORS for Chrome Extension
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+# Global variables for models
+tokenizer = None
+model = None
+t5_tokenizer = None
+t5_model = None
+device = None
+
+@app.on_event("startup")
+async def load_models():
+    """Load models on startup"""
+    global tokenizer, model, t5_tokenizer, t5_model, device
+    
+    device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+    print(f"Using device: {device}")
+    
+    # Load DistilBERT
+    print("Loading DistilBERT model...")
+    tokenizer = AutoTokenizer.from_pretrained("distilbert-base-uncased")
+    model = AutoModelForSequenceClassification.from_pretrained(
+        "./clickbait_detector_model"
+    )
+    model.to(device)
+    model.eval()
+    print("✓ DistilBERT loaded")
+    
+    # Load T5
+    print("Loading T5 model...")
+    t5_tokenizer = T5Tokenizer.from_pretrained("t5-base")
+    t5_model = T5ForConditionalGeneration.from_pretrained(
+        "./t5_clickbait_rewriter_finetuned"
+    )
+    t5_model.to(device)
+    t5_model.eval()
+    print("✓ T5 loaded")
+
+class HeadlineRequest(BaseModel):
+    headline: str
+
+@app.get("/")
+def root():
+    return {
+        "name": "Clickbait Detector API",
+        "version": "1.0.0",
+        "status": "online",
+        "endpoints": {
+            "/detect": "POST - Detect if headline is clickbait",
+            "/rewrite": "POST - Rewrite clickbait to neutral",
+            "/analyze": "POST - Detect + Rewrite in one call"
+        }
+    }
+
+@app.post("/detect")
+def detect_clickbait(request: HeadlineRequest):
+    """Detect if headline is clickbait"""
+    try:
+        inputs = tokenizer(
+            request.headline,
+            return_tensors='pt',
+            padding=True,
+            truncation=True,
+            max_length=128
+        ).to(device)
+        
+        with torch.no_grad():
+            outputs = model(**inputs)
+            probs = torch.softmax(outputs.logits, dim=1)
+            prediction = torch.argmax(probs, dim=1).item()
+            confidence = probs[0][prediction].item()
+        
+        return {
+            "headline": request.headline,
+            "is_clickbait": bool(prediction),
+            "confidence": float(confidence),
+            "label": "clickbait" if prediction else "neutral"
+        }
+    except Exception as e:
+        return {"error": str(e)}
+
+@app.post("/rewrite")
+def rewrite_headline(request: HeadlineRequest):
+    """Rewrite clickbait headline to neutral"""
+    try:
+        prompt = f"rewrite clickbait to neutral: {request.headline}"
+        inputs = t5_tokenizer(
+            prompt,
+            return_tensors='pt',
+            max_length=128,
+            truncation=True
+        ).to(device)
+        
+        with torch.no_grad():
+            outputs = t5_model.generate(
+                inputs['input_ids'],
+                max_length=64,
+                num_beams=5,
+                early_stopping=True
+            )
+        
+        neutral = t5_tokenizer.decode(outputs[0], skip_special_tokens=True)
+        
+        return {
+            "original": request.headline,
+            "rewritten": neutral
+        }
+    except Exception as e:
+        return {"error": str(e)}
+
+@app.post("/analyze")
+def analyze_headline(request: HeadlineRequest):
+    """Detect and rewrite in one call"""
+    try:
+        detection = detect_clickbait(request)
+        
+        if detection.get("is_clickbait"):
+            rewrite = rewrite_headline(request)
+            return {
+                **detection,
+                "rewritten": rewrite.get("rewritten")
+            }
+        else:
+            return {
+                **detection,
+                "rewritten": request.headline,
+                "message": "Headline is already neutral"
+            }
+    except Exception as e:
+        return {"error": str(e)}

requirements.txt

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+fastapi==0.104.1
+uvicorn==0.24.0
+transformers==4.35.2
+torch==2.1.0
+pydantic==2.5.0